code: def test_combination_of_two_different_berts(self):
      # Regular BERT, but with AlBERT's special compressed embedding scheme
  
      class AlmostRegularTransformer(TokenEmbedder):
          def __init__(self):
              super().__init__()
              self.embeddings = AutoModel.from_pretrained(
                  "albert-base-v2"
              ).embeddings
              self.transformer = (
                  TransformerStack.from_pretrained_module(
                      "bert-base-cased", relevant_module="bert.encoder"
                  )
              )
              # We want to tune only the embeddings, because that's our experiment.
              self.transformer.requires_grad = False
  
          def forward(
              self, token_ids: torch.LongTensor, mask: torch.BoolTensor
          ):
              x = self.embeddings(token_ids, mask)
              x = self.transformer(x)
              return x
  
      almost = AlmostRegularTransformer()
      assert len(almost.transformer.layers) == 12
      assert isinstance(almost.embeddings, AlbertEmbeddings)
  
file_path: allennlp/tests/modules/transformer/toolkit_test.py
imports: import pytest
  import torch
  from torch.testing import assert_allclose
  from transformers import AutoModel
  from transformers.models.albert.modeling_albert import AlbertEmbeddings
  from allennlp.common import cached_transformers
  from allennlp.data.vocabulary import Vocabulary
  from allennlp.modules.token_embedders import Embedding, TokenEmbedder
  from allennlp.modules.transformer import TransformerStack, TransformerEmbeddings, TransformerPooler
  from allennlp.common.testing import AllenNlpTestCase
label_desc: Use image node_id 5 for calling the TestTransformerToolkit obj's underlying member method code with example usage: obj.test_combination_of_two_different_berts() and returns: x